ArticleJournal of medical genetics2015
A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects.
Article in Journal of medical genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 60 citations in OpenAlex.
- Using Polygenic Risk Scores to Refine Risk and Outcomes in High-grade Serous Ovarian Cancer in Australia.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Validation of a Clinical and Polygenic Risk Prediction Model for Ovarian Cancer in the Nurses' Health Study.Cancer prevention research (Philadelphia, Pa.) · 2026Article
- Adapting the BOADICEA breast and ovarian cancer risk models for the ethnically diverse UK population.British journal of cancer · 2025Article
- Defining Lifetime Risk Thresholds for Breast Cancer Surgical Prevention.JAMA oncology · 2025Article
- Estimating sojourn time and sensitivity of screening for ovarian cancer using a Bayesian framework.Journal of the National Cancer Institute · 2024Article
- Economic Evaluation of Population-Based BRCA1 and BRCA2 Testing in Canada.JAMA network open · 2024Article
- A comprehensive analysis of germline predisposition to early-onset ovarian cancer.Scientific reports · 2024Article
- Article
- Cost-Effectiveness of Population-Based Multigene Testing for Breast and Ovarian Cancer Prevention.JAMA network open · 2024Article
- Developmental genetic effects on externalizing behavior and alcohol use: Examination across two longitudinal samples.Development and psychopathology · 2024Article
- A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study).Scientific reports · 2024Article
- Validation of the BOADICEA model for predicting the likelihood of carrying pathogenic variants in eight breast and ovarian cancer susceptibility genes.Scientific reports · 2023Article
- Enhancing the BOADICEA cancer risk prediction model to incorporate new data onJournal of medical genetics · 2022Article
- Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors.Journal of medical genetics · 2022Article
- Unselected Population Genetic Testing for Personalised Ovarian Cancer Risk Prediction: A Qualitative Study Using Semi-Structured Interviews.Diagnostics (Basel, Switzerland) · 2022Article
- The DNA methylome of cervical cells can predict the presence of ovarian cancer.Nature communications · 2022Observational
- New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers.Cancers · 2021Review
- Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models.JNCI cancer spectrum · 2021Article
- CanRisk Tool-A Web Interface for the Prediction of Breast and Ovarian Cancer Risk and the Likelihood of Carrying Genetic Pathogenic Variants.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundAlthough BRCA1 and BRCA2 mutations account for only ∼27% of the familial aggregation of ovarian cancer (OvC), no OvC risk prediction model currently exists that considers the effects of BRCA1, BRCA2 and other familial factors. Therefore, a currently unresolved problem in clinical genetics is how to counsel women with family history of OvC but no identifiable BRCA1/2 mutations.
methodsWe used data from 1548 patients with OvC and their relatives from a population-based study, with known BRCA1/2 mutation status, to investigate OvC genetic susceptibility models, using segregation analysis methods.
resultsThe most parsimonious model included the effects of BRCA1/2 mutations, and the residual familial aggregation was accounted for by a polygenic component (SD 1.43, 95% CI 1.10 to 1.86), reflecting the multiplicative effects of a large number of genes with small contributions to the familial risk. We estimated that 1 in 630 individuals carries a BRCA1 mutation and 1 in 195 carries a BRCA2 mutation. We extended this model to incorporate the explicit effects of 17 common alleles that are associated with OvC risk. Based on our models, assuming all of the susceptibility genes could be identified we estimate that the half of the female population at highest genetic risk will account for 92% of all OvCs.
conclusionsThe resulting model can be used to obtain the risk of developing OvC on the basis of BRCA1/2, explicit family history and common alleles. This is the first model that accounts for all OvC familial aggregation and would be useful in the OvC genetic counselling process.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.